{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "provenance": []
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    },
    "language_info": {
      "name": "python"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "<a href=\"https://colab.research.google.com/drive/1ouo-e4IwgGj1cs6e0M1ZULz-UpNt2KXw?usp=sharing\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"></a>\n",
        "\n",
        "### [LangGraph](https://langchain-ai.github.io/langgraph/tutorials/introduction/) based Agentic Workflow - Data Analysis\n",
        "\n",
        "### 📌 Overview\n",
        "\n",
        "This sample provides a simple, function-based approach to analyzing data using AI with LangGraph. It allows you to load data (from local files or the web), visualize it, and generate AI-powered analysis without writing complex code.\n",
        "\n",
        "### 🛠️ How LangGraph Works in This Project\n",
        "\n",
        "LangGraph is a framework for building stateful applications with language models. In this project, we use LangGraph to create a simple workflow that processes data in several steps:\n",
        "\n",
        "#### 1. State Management\n",
        "\n",
        "The workflow maintains state using a simple schema:\n",
        "\n",
        "```python\n",
        "class AnalysisState(langchain.pydantic_v1.BaseModel):\n",
        "    data_context: str             # Summary of the dataset\n",
        "    analysis_results: str = None  # Data analysis results\n",
        "    business_insights: str = None # Business recommendations\n",
        "    final_report: str = None      # Final combined report\n",
        "```\n",
        "\n",
        "#### 2. Graph Structure\n",
        "\n",
        "The LangGraph workflow consists of three nodes connected in sequence:\n",
        "\n",
        "1. **Data Analysis Node**: Analyzes the dataset statistics and patterns\n",
        "2. **Business Insights Node**: Generates actionable recommendations based on analysis\n",
        "3. **Final Report Node**: Combines analysis and insights into a cohesive report\n",
        "\n",
        "#### 3. Node Implementation\n",
        "\n",
        "Each node follows the same pattern:\n",
        "1. Takes the current state as input\n",
        "2. Creates a prompt using a template\n",
        "3. Sends the prompt to the language model\n",
        "4. Extracts the response\n",
        "5. Updates and returns the new state\n",
        "\n",
        "#### 4. Workflow Execution\n",
        "\n",
        "The workflow runs through each node in sequence:\n",
        "```\n",
        "Data Context → Analysis → Insights → Report\n",
        "```\n",
        "\n",
        "Each step builds on the previous, creating a complete analysis pipeline.\n",
        "\n"
      ],
      "metadata": {
        "id": "XpBbSOxMsUNl"
      }
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Install required libraries"
      ],
      "metadata": {
        "id": "Y3axTI0sp5Hg"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "!pip install -q -U langgraph langchain langchain-groq pandas numpy plotly matplotlib seaborn ipython"
      ],
      "metadata": {
        "id": "ShxTNxM5gqtr",
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "outputId": "0d6262f0-9c47-4197-ccc7-5846b896b277"
      },
      "execution_count": 1,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m89.9/89.9 kB\u001b[0m \u001b[31m1.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K     \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m62.0/62.0 kB\u001b[0m \u001b[31m840.3 kB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m151.4/151.4 kB\u001b[0m \u001b[31m6.1 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.1/13.1 MB\u001b[0m \u001b[31m20.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m14.8/14.8 MB\u001b[0m \u001b[31m24.8 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m825.5/825.5 kB\u001b[0m \u001b[31m31.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m121.9/121.9 kB\u001b[0m \u001b[31m6.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m1.6/1.6 MB\u001b[0m \u001b[31m13.9 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m45.4/45.4 kB\u001b[0m \u001b[31m1.5 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m85.4/85.4 kB\u001b[0m \u001b[31m1.7 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m\n",
            "\u001b[?25h\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\n",
            "google-colab 1.0.0 requires ipython==7.34.0, but you have ipython 8.32.0 which is incompatible.\n",
            "google-colab 1.0.0 requires pandas==2.2.2, but you have pandas 2.2.3 which is incompatible.\u001b[0m\u001b[31m\n",
            "\u001b[0m"
          ]
        }
      ]
    },
    {
      "cell_type": "markdown",
      "source": [
        "### Import Groq related libraries"
      ],
      "metadata": {
        "id": "9jJ1vqs-p_Zx"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import pandas as pd\n",
        "import numpy as np\n",
        "import plotly.express as px\n",
        "import plotly.graph_objects as go\n",
        "from typing import Dict, List, Any, Tuple, Optional\n",
        "import tempfile\n",
        "import json\n",
        "import os\n",
        "from datetime import datetime\n",
        "import matplotlib.pyplot as plt\n",
        "import seaborn as sns\n",
        "\n",
        "# LangGraph imports\n",
        "from langgraph.graph import StateGraph, END\n",
        "import langchain\n",
        "from langchain.prompts import PromptTemplate\n",
        "from langchain_groq import ChatGroq\n",
        "import langchain.pydantic_v1"
      ],
      "metadata": {
        "id": "RL-3LsYogoH5"
      },
      "execution_count": 24,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "import getpass\n",
        "import os"
      ],
      "metadata": {
        "id": "GT55z5AkhyOW"
      },
      "execution_count": 5,
      "outputs": []
    },
    {
      "cell_type": "markdown",
      "source": [
        "#### Provide a Groq API key. You can create one to access free open-source models at the following link.\n",
        "\n",
        "[Groq API Creation Link](https://console.groq.com/keys)\n",
        "\n",
        "\n"
      ],
      "metadata": {
        "id": "F6UeDlrgqI2A"
      }
    },
    {
      "cell_type": "code",
      "source": [
        "os.environ[\"GROQ_API_KEY\"] = getpass.getpass()"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/"
        },
        "id": "yobvrD3glfd4",
        "outputId": "27c29dcf-1fe2-4e50-a6f6-6283dcb2f27f"
      },
      "execution_count": 6,
      "outputs": [
        {
          "name": "stdout",
          "output_type": "stream",
          "text": [
            "··········\n"
          ]
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [
        "def get_data_summary(df):\n",
        "    \"\"\"Generate a summary of the dataset.\n",
        "\n",
        "    Args:\n",
        "        df: Pandas DataFrame to summarize\n",
        "\n",
        "    Returns:\n",
        "        A string containing the dataset summary\n",
        "    \"\"\"\n",
        "    # Basic dataset information\n",
        "    summary = f\"\"\"\n",
        "    Dataset Summary:\n",
        "    - Rows: {len(df)}\n",
        "    - Columns: {', '.join(df.columns.tolist())}\n",
        "    \"\"\"\n",
        "\n",
        "    # Column information\n",
        "    for col in df.columns:\n",
        "        if pd.api.types.is_numeric_dtype(df[col]):\n",
        "            summary += f\"\"\"\n",
        "            {col}:\n",
        "            - Type: Numeric\n",
        "            - Range: {df[col].min()} to {df[col].max()}\n",
        "            - Average: {df[col].mean():.2f}\n",
        "            - Missing: {df[col].isnull().sum()}\n",
        "            \"\"\"\n",
        "        else:\n",
        "            summary += f\"\"\"\n",
        "            {col}:\n",
        "            - Type: Categorical/Text\n",
        "            - Unique Values: {df[col].nunique()}\n",
        "            - Top Values: {', '.join(df[col].value_counts().nlargest(3).index.astype(str))}\n",
        "            - Missing: {df[col].isnull().sum()}\n",
        "            \"\"\"\n",
        "\n",
        "    return summary"
      ],
      "metadata": {
        "id": "UUdnlanFXi60"
      },
      "execution_count": 16,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def create_analysis_workflow(llm, data_context):\n",
        "    \"\"\"Create a LangGraph workflow for data analysis.\n",
        "\n",
        "    Args:\n",
        "        llm: Language model to use\n",
        "        data_context: Summary of the dataset\n",
        "\n",
        "    Returns:\n",
        "        A compiled LangGraph workflow\n",
        "    \"\"\"\n",
        "    # Define what our state will track\n",
        "    class AnalysisState(langchain.pydantic_v1.BaseModel):\n",
        "        data_context: str\n",
        "        analysis_results: str = None\n",
        "        business_insights: str = None\n",
        "        final_report: str = None\n",
        "\n",
        "    # Create our graph\n",
        "    workflow = StateGraph(AnalysisState)\n",
        "\n",
        "    # Node 1: Analyze the data\n",
        "    def analyze_data(state):\n",
        "        prompt = \"\"\"You are a data analyst. Analyze this dataset:\n",
        "        {data_context}\n",
        "\n",
        "        Provide key statistical findings, distribution analysis, and relationship analysis.\n",
        "        Use specific numbers and clear explanations.\n",
        "        \"\"\"\n",
        "\n",
        "        chain = PromptTemplate.from_template(prompt) | llm\n",
        "        response = chain.invoke({\"data_context\": state.data_context})\n",
        "\n",
        "        if hasattr(response, 'content'):\n",
        "            analysis = response.content\n",
        "        else:\n",
        "            analysis = str(response)\n",
        "\n",
        "        return {\"analysis_results\": analysis}\n",
        "\n",
        "    # Node 2: Generate business insights\n",
        "    def generate_insights(state):\n",
        "        prompt = \"\"\"You are a business consultant. Based on this data analysis:\n",
        "        {analysis_results}\n",
        "\n",
        "        Generate actionable business insights including key findings, strategic\n",
        "        recommendations, and implementation guidelines.\n",
        "        \"\"\"\n",
        "\n",
        "        chain = PromptTemplate.from_template(prompt) | llm\n",
        "        response = chain.invoke({\"analysis_results\": state.analysis_results})\n",
        "\n",
        "        if hasattr(response, 'content'):\n",
        "            insights = response.content\n",
        "        else:\n",
        "            insights = str(response)\n",
        "\n",
        "        return {\"business_insights\": insights}\n",
        "\n",
        "    # Node 3: Create final report\n",
        "    def create_report(state):\n",
        "        prompt = \"\"\"Combine this analysis and insights into a clear final report:\n",
        "\n",
        "        ANALYSIS:\n",
        "        {analysis_results}\n",
        "\n",
        "        INSIGHTS:\n",
        "        {business_insights}\n",
        "\n",
        "        Create a comprehensive report with clear sections and highlights.\n",
        "        \"\"\"\n",
        "\n",
        "        chain = PromptTemplate.from_template(prompt) | llm\n",
        "        response = chain.invoke({\n",
        "            \"analysis_results\": state.analysis_results,\n",
        "            \"business_insights\": state.business_insights\n",
        "        })\n",
        "\n",
        "        if hasattr(response, 'content'):\n",
        "            report = response.content\n",
        "        else:\n",
        "            report = str(response)\n",
        "\n",
        "        return {\"final_report\": report}\n",
        "\n",
        "    # Add nodes to graph\n",
        "    workflow.add_node(\"analyze\", analyze_data)\n",
        "    workflow.add_node(\"insights\", generate_insights)\n",
        "    workflow.add_node(\"report\", create_report)\n",
        "\n",
        "    # Connect the nodes\n",
        "    workflow.add_edge(\"analyze\", \"insights\")\n",
        "    workflow.add_edge(\"insights\", \"report\")\n",
        "    workflow.add_edge(\"report\", END)\n",
        "\n",
        "    # Set starting point\n",
        "    workflow.set_entry_point(\"analyze\")\n",
        "\n",
        "    # Compile the graph\n",
        "    return workflow.compile()"
      ],
      "metadata": {
        "id": "GU04mYBVooZI"
      },
      "execution_count": 17,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def analyze_data(df, provider=\"OpenAI\", model=\"gpt-4o\"):\n",
        "    \"\"\"Analyze a dataframe using LangGraph and LLMs.\n",
        "\n",
        "    Args:\n",
        "        df: Pandas DataFrame to analyze\n",
        "        provider: \"OpenAI\" or \"Anthropic\"\n",
        "        model: Model name\n",
        "\n",
        "    Returns:\n",
        "        Dictionary with analysis results and business insights\n",
        "    \"\"\"\n",
        "    # Set up the LLM\n",
        "    llm = llm = ChatGroq(\n",
        "        model=\"llama3-8b-8192\",\n",
        "        temperature=0.5\n",
        "    )\n",
        "\n",
        "    # Get dataset summary\n",
        "    data_context = get_data_summary(df)\n",
        "\n",
        "    # Create and run the analysis workflow\n",
        "    workflow = create_analysis_workflow(llm, data_context)\n",
        "\n",
        "    # Run the workflow\n",
        "    results = workflow.invoke({\n",
        "        \"data_context\": data_context,\n",
        "        \"analysis_results\": None,\n",
        "        \"business_insights\": None,\n",
        "        \"final_report\": None\n",
        "    })\n",
        "\n",
        "    # Return the results\n",
        "    return {\n",
        "        \"analysis\": results.get(\"analysis_results\", \"No analysis available\"),\n",
        "        \"insights\": results.get(\"business_insights\", \"No insights available\"),\n",
        "        \"report\": results.get(\"final_report\", \"No report available\")\n",
        "    }"
      ],
      "metadata": {
        "id": "zX7kCqtJormQ"
      },
      "execution_count": 18,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def visualize_data(df):\n",
        "    \"\"\"Create basic visualizations of the dataframe.\n",
        "\n",
        "    Args:\n",
        "        df: Pandas DataFrame to visualize\n",
        "    \"\"\"\n",
        "    print(\"Creating visualizations...\")\n",
        "\n",
        "    # Identify numeric columns\n",
        "    numeric_cols = df.select_dtypes(include=['int64', 'float64']).columns\n",
        "\n",
        "    # Create plots\n",
        "    if len(numeric_cols) >= 1:\n",
        "        # Distribution of first numeric column\n",
        "        plt.figure(figsize=(10, 6))\n",
        "        sns.histplot(df[numeric_cols[0]], kde=True)\n",
        "        plt.title(f'Distribution of {numeric_cols[0]}')\n",
        "        plt.show()\n",
        "\n",
        "    if len(numeric_cols) >= 2:\n",
        "        # Scatter plot of first two numeric columns\n",
        "        plt.figure(figsize=(10, 6))\n",
        "        sns.scatterplot(x=df[numeric_cols[0]], y=df[numeric_cols[1]])\n",
        "        plt.title(f'{numeric_cols[0]} vs {numeric_cols[1]}')\n",
        "        plt.show()\n",
        "\n",
        "    # Correlation heatmap for numeric columns\n",
        "    if len(numeric_cols) > 1:\n",
        "        plt.figure(figsize=(12, 8))\n",
        "        corr = df[numeric_cols].corr()\n",
        "        sns.heatmap(corr, annot=True, cmap='coolwarm')\n",
        "        plt.title('Correlation Matrix')\n",
        "        plt.show()"
      ],
      "metadata": {
        "id": "gVJB-_ZIo0XO"
      },
      "execution_count": 19,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "def show_data_summary(df):\n",
        "    \"\"\"Display a summary of the dataframe.\n",
        "\n",
        "    Args:\n",
        "        df: Pandas DataFrame to summarize\n",
        "    \"\"\"\n",
        "    print(f\"Dataset shape: {df.shape[0]} rows x {df.shape[1]} columns\")\n",
        "    print(\"\\nSample data:\")\n",
        "    display(df.head())\n",
        "\n",
        "    print(\"\\nData types:\")\n",
        "    display(df.dtypes)\n",
        "\n",
        "    print(\"\\nSummary statistics:\")\n",
        "    display(df.describe())\n",
        "\n",
        "    print(\"\\nMissing values:\")\n",
        "    missing = df.isnull().sum()\n",
        "    display(missing[missing > 0] if any(missing > 0) else \"No missing values\")"
      ],
      "metadata": {
        "id": "09xFV339oxg_"
      },
      "execution_count": 20,
      "outputs": []
    },
    {
      "cell_type": "code",
      "source": [
        "# 1. Import libraries and load data\n",
        "import pandas as pd\n",
        "\n",
        "# Load your data\n",
        "df = pd.read_csv('https://raw.githubusercontent.com/mwaskom/seaborn-data/master/iris.csv')\n",
        "\n",
        "# 2. Display data summary\n",
        "show_data_summary(df)\n",
        "\n",
        "# 3. Create visualizations\n",
        "visualize_data(df)\n",
        "\n",
        "# 4. Analyze the data\n",
        "results = analyze_data(df)\n",
        "\n",
        "# 5. Display the results\n",
        "from IPython.display import Markdown\n",
        "\n",
        "display(Markdown(\"## Data Analysis\"))\n",
        "display(Markdown(results[\"analysis\"]))\n",
        "\n",
        "display(Markdown(\"## Business Insights\"))\n",
        "display(Markdown(results[\"insights\"]))\n",
        "\n",
        "display(Markdown(\"## Complete Report\"))\n",
        "display(Markdown(results[\"report\"]))\n"
      ],
      "metadata": {
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        },
        "id": "RKa21U3No_xC",
        "outputId": "88f6e1ab-3c9f-4d7d-cf7e-c3b858c5321f"
      },
      "execution_count": 25,
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Dataset shape: 150 rows x 5 columns\n",
            "\n",
            "Sample data:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "   sepal_length  sepal_width  petal_length  petal_width species\n",
              "0           5.1          3.5           1.4          0.2  setosa\n",
              "1           4.9          3.0           1.4          0.2  setosa\n",
              "2           4.7          3.2           1.3          0.2  setosa\n",
              "3           4.6          3.1           1.5          0.2  setosa\n",
              "4           5.0          3.6           1.4          0.2  setosa"
            ],
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              "\n",
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              "    <tr style=\"text-align: right;\">\n",
              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "      <th>species</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>0</th>\n",
              "      <td>5.1</td>\n",
              "      <td>3.5</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>1</th>\n",
              "      <td>4.9</td>\n",
              "      <td>3.0</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>2</th>\n",
              "      <td>4.7</td>\n",
              "      <td>3.2</td>\n",
              "      <td>1.3</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>3</th>\n",
              "      <td>4.6</td>\n",
              "      <td>3.1</td>\n",
              "      <td>1.5</td>\n",
              "      <td>0.2</td>\n",
              "      <td>setosa</td>\n",
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              "    <tr>\n",
              "      <th>4</th>\n",
              "      <td>5.0</td>\n",
              "      <td>3.6</td>\n",
              "      <td>1.4</td>\n",
              "      <td>0.2</td>\n",
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              "\n",
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              "  </style>\n",
              "\n",
              "    <script>\n",
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              "        document.querySelector('#df-ad1d22b4-c095-4679-9d58-c35f0905c1d7 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
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              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
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              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
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              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
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              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
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              "    background-color: var(--disabled-bg-color);\n",
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              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
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              "      border-bottom-color: var(--fill-color);\n",
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              "      border-right-color: var(--fill-color);\n",
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              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
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              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
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              "</div>\n",
              "\n",
              "    </div>\n",
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            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(Markdown(results[\\\"report\\\"]))\",\n  \"rows\": 5,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2073644135332772,\n        \"min\": 4.6,\n        \"max\": 5.1,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          4.9,\n          5.0,\n          4.7\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.2588435821108957,\n        \"min\": 3.0,\n        \"max\": 3.6,\n        \"num_unique_values\": 5,\n        \"samples\": [\n          3.0,\n          3.6,\n          3.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.07071067811865474,\n        \"min\": 1.3,\n        \"max\": 1.5,\n        \"num_unique_values\": 3,\n        \"samples\": [\n          1.4,\n          1.3,\n          1.5\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 0.0,\n        \"min\": 0.2,\n        \"max\": 0.2,\n        \"num_unique_values\": 1,\n        \"samples\": [\n          0.2\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"species\",\n      \"properties\": {\n        \"dtype\": \"category\",\n        \"num_unique_values\": 1,\n        \"samples\": [\n          \"setosa\"\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
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          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Data types:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "sepal_length    float64\n",
              "sepal_width     float64\n",
              "petal_length    float64\n",
              "petal_width     float64\n",
              "species          object\n",
              "dtype: object"
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              "</div><br><label><b>dtype:</b> object</label>"
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          "metadata": {}
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        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Summary statistics:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "       sepal_length  sepal_width  petal_length  petal_width\n",
              "count    150.000000   150.000000    150.000000   150.000000\n",
              "mean       5.843333     3.057333      3.758000     1.199333\n",
              "std        0.828066     0.435866      1.765298     0.762238\n",
              "min        4.300000     2.000000      1.000000     0.100000\n",
              "25%        5.100000     2.800000      1.600000     0.300000\n",
              "50%        5.800000     3.000000      4.350000     1.300000\n",
              "75%        6.400000     3.300000      5.100000     1.800000\n",
              "max        7.900000     4.400000      6.900000     2.500000"
            ],
            "text/html": [
              "\n",
              "  <div id=\"df-9921a078-eca6-416b-89d8-6d20cc7849d0\" class=\"colab-df-container\">\n",
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              "\n",
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              "        text-align: right;\n",
              "    }\n",
              "</style>\n",
              "<table border=\"1\" class=\"dataframe\">\n",
              "  <thead>\n",
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              "      <th></th>\n",
              "      <th>sepal_length</th>\n",
              "      <th>sepal_width</th>\n",
              "      <th>petal_length</th>\n",
              "      <th>petal_width</th>\n",
              "    </tr>\n",
              "  </thead>\n",
              "  <tbody>\n",
              "    <tr>\n",
              "      <th>count</th>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "      <td>150.000000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>mean</th>\n",
              "      <td>5.843333</td>\n",
              "      <td>3.057333</td>\n",
              "      <td>3.758000</td>\n",
              "      <td>1.199333</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>std</th>\n",
              "      <td>0.828066</td>\n",
              "      <td>0.435866</td>\n",
              "      <td>1.765298</td>\n",
              "      <td>0.762238</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>min</th>\n",
              "      <td>4.300000</td>\n",
              "      <td>2.000000</td>\n",
              "      <td>1.000000</td>\n",
              "      <td>0.100000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>25%</th>\n",
              "      <td>5.100000</td>\n",
              "      <td>2.800000</td>\n",
              "      <td>1.600000</td>\n",
              "      <td>0.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>50%</th>\n",
              "      <td>5.800000</td>\n",
              "      <td>3.000000</td>\n",
              "      <td>4.350000</td>\n",
              "      <td>1.300000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>75%</th>\n",
              "      <td>6.400000</td>\n",
              "      <td>3.300000</td>\n",
              "      <td>5.100000</td>\n",
              "      <td>1.800000</td>\n",
              "    </tr>\n",
              "    <tr>\n",
              "      <th>max</th>\n",
              "      <td>7.900000</td>\n",
              "      <td>4.400000</td>\n",
              "      <td>6.900000</td>\n",
              "      <td>2.500000</td>\n",
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              "\n",
              "    [theme=dark] .colab-df-convert {\n",
              "      background-color: #3B4455;\n",
              "      fill: #D2E3FC;\n",
              "    }\n",
              "\n",
              "    [theme=dark] .colab-df-convert:hover {\n",
              "      background-color: #434B5C;\n",
              "      box-shadow: 0px 1px 3px 1px rgba(0, 0, 0, 0.15);\n",
              "      filter: drop-shadow(0px 1px 2px rgba(0, 0, 0, 0.3));\n",
              "      fill: #FFFFFF;\n",
              "    }\n",
              "  </style>\n",
              "\n",
              "    <script>\n",
              "      const buttonEl =\n",
              "        document.querySelector('#df-9921a078-eca6-416b-89d8-6d20cc7849d0 button.colab-df-convert');\n",
              "      buttonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "\n",
              "      async function convertToInteractive(key) {\n",
              "        const element = document.querySelector('#df-9921a078-eca6-416b-89d8-6d20cc7849d0');\n",
              "        const dataTable =\n",
              "          await google.colab.kernel.invokeFunction('convertToInteractive',\n",
              "                                                    [key], {});\n",
              "        if (!dataTable) return;\n",
              "\n",
              "        const docLinkHtml = 'Like what you see? Visit the ' +\n",
              "          '<a target=\"_blank\" href=https://colab.research.google.com/notebooks/data_table.ipynb>data table notebook</a>'\n",
              "          + ' to learn more about interactive tables.';\n",
              "        element.innerHTML = '';\n",
              "        dataTable['output_type'] = 'display_data';\n",
              "        await google.colab.output.renderOutput(dataTable, element);\n",
              "        const docLink = document.createElement('div');\n",
              "        docLink.innerHTML = docLinkHtml;\n",
              "        element.appendChild(docLink);\n",
              "      }\n",
              "    </script>\n",
              "  </div>\n",
              "\n",
              "\n",
              "<div id=\"df-83551009-1a9a-4da0-bc8a-82c42a91c652\">\n",
              "  <button class=\"colab-df-quickchart\" onclick=\"quickchart('df-83551009-1a9a-4da0-bc8a-82c42a91c652')\"\n",
              "            title=\"Suggest charts\"\n",
              "            style=\"display:none;\">\n",
              "\n",
              "<svg xmlns=\"http://www.w3.org/2000/svg\" height=\"24px\"viewBox=\"0 0 24 24\"\n",
              "     width=\"24px\">\n",
              "    <g>\n",
              "        <path d=\"M19 3H5c-1.1 0-2 .9-2 2v14c0 1.1.9 2 2 2h14c1.1 0 2-.9 2-2V5c0-1.1-.9-2-2-2zM9 17H7v-7h2v7zm4 0h-2V7h2v10zm4 0h-2v-4h2v4z\"/>\n",
              "    </g>\n",
              "</svg>\n",
              "  </button>\n",
              "\n",
              "<style>\n",
              "  .colab-df-quickchart {\n",
              "      --bg-color: #E8F0FE;\n",
              "      --fill-color: #1967D2;\n",
              "      --hover-bg-color: #E2EBFA;\n",
              "      --hover-fill-color: #174EA6;\n",
              "      --disabled-fill-color: #AAA;\n",
              "      --disabled-bg-color: #DDD;\n",
              "  }\n",
              "\n",
              "  [theme=dark] .colab-df-quickchart {\n",
              "      --bg-color: #3B4455;\n",
              "      --fill-color: #D2E3FC;\n",
              "      --hover-bg-color: #434B5C;\n",
              "      --hover-fill-color: #FFFFFF;\n",
              "      --disabled-bg-color: #3B4455;\n",
              "      --disabled-fill-color: #666;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart {\n",
              "    background-color: var(--bg-color);\n",
              "    border: none;\n",
              "    border-radius: 50%;\n",
              "    cursor: pointer;\n",
              "    display: none;\n",
              "    fill: var(--fill-color);\n",
              "    height: 32px;\n",
              "    padding: 0;\n",
              "    width: 32px;\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart:hover {\n",
              "    background-color: var(--hover-bg-color);\n",
              "    box-shadow: 0 1px 2px rgba(60, 64, 67, 0.3), 0 1px 3px 1px rgba(60, 64, 67, 0.15);\n",
              "    fill: var(--button-hover-fill-color);\n",
              "  }\n",
              "\n",
              "  .colab-df-quickchart-complete:disabled,\n",
              "  .colab-df-quickchart-complete:disabled:hover {\n",
              "    background-color: var(--disabled-bg-color);\n",
              "    fill: var(--disabled-fill-color);\n",
              "    box-shadow: none;\n",
              "  }\n",
              "\n",
              "  .colab-df-spinner {\n",
              "    border: 2px solid var(--fill-color);\n",
              "    border-color: transparent;\n",
              "    border-bottom-color: var(--fill-color);\n",
              "    animation:\n",
              "      spin 1s steps(1) infinite;\n",
              "  }\n",
              "\n",
              "  @keyframes spin {\n",
              "    0% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "      border-left-color: var(--fill-color);\n",
              "    }\n",
              "    20% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    30% {\n",
              "      border-color: transparent;\n",
              "      border-left-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    40% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-top-color: var(--fill-color);\n",
              "    }\n",
              "    60% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "    }\n",
              "    80% {\n",
              "      border-color: transparent;\n",
              "      border-right-color: var(--fill-color);\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "    90% {\n",
              "      border-color: transparent;\n",
              "      border-bottom-color: var(--fill-color);\n",
              "    }\n",
              "  }\n",
              "</style>\n",
              "\n",
              "  <script>\n",
              "    async function quickchart(key) {\n",
              "      const quickchartButtonEl =\n",
              "        document.querySelector('#' + key + ' button');\n",
              "      quickchartButtonEl.disabled = true;  // To prevent multiple clicks.\n",
              "      quickchartButtonEl.classList.add('colab-df-spinner');\n",
              "      try {\n",
              "        const charts = await google.colab.kernel.invokeFunction(\n",
              "            'suggestCharts', [key], {});\n",
              "      } catch (error) {\n",
              "        console.error('Error during call to suggestCharts:', error);\n",
              "      }\n",
              "      quickchartButtonEl.classList.remove('colab-df-spinner');\n",
              "      quickchartButtonEl.classList.add('colab-df-quickchart-complete');\n",
              "    }\n",
              "    (() => {\n",
              "      let quickchartButtonEl =\n",
              "        document.querySelector('#df-83551009-1a9a-4da0-bc8a-82c42a91c652 button');\n",
              "      quickchartButtonEl.style.display =\n",
              "        google.colab.kernel.accessAllowed ? 'block' : 'none';\n",
              "    })();\n",
              "  </script>\n",
              "</div>\n",
              "\n",
              "    </div>\n",
              "  </div>\n"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "dataframe",
              "summary": "{\n  \"name\": \"display(Markdown(results[\\\"report\\\"]))\",\n  \"rows\": 8,\n  \"fields\": [\n    {\n      \"column\": \"sepal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.24711349471842,\n        \"min\": 0.8280661279778629,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          5.843333333333334,\n          5.8,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"sepal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.08617800869866,\n        \"min\": 0.435866284936698,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.0573333333333337,\n          3.0,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_length\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 51.83521261418364,\n        \"min\": 1.0,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          3.7580000000000005,\n          4.35,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    },\n    {\n      \"column\": \"petal_width\",\n      \"properties\": {\n        \"dtype\": \"number\",\n        \"std\": 52.636648242617504,\n        \"min\": 0.1,\n        \"max\": 150.0,\n        \"num_unique_values\": 8,\n        \"samples\": [\n          1.1993333333333336,\n          1.3,\n          150.0\n        ],\n        \"semantic_type\": \"\",\n        \"description\": \"\"\n      }\n    }\n  ]\n}"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "\n",
            "Missing values:\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "'No missing values'"
            ],
            "application/vnd.google.colaboratory.intrinsic+json": {
              "type": "string"
            }
          },
          "metadata": {}
        },
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Creating visualizations...\n"
          ]
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1000x600 with 1 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<Figure size 1200x800 with 2 Axes>"
            ],
            "image/png": 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\n"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## Data Analysis"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "Based on the provided dataset, I'll summarize the key statistical findings, distribution analysis, and relationship analysis.\n\n**Descriptive Statistics:**\n\n1. **Sepal Length:** The average sepal length is 5.84, with a range of 4.3 to 7.9. The median sepal length is 5.8, indicating that the data is slightly skewed to the right.\n2. **Sepal Width:** The average sepal width is 3.06, with a range of 2.0 to 4.4. The median sepal width is 3.0, suggesting a relatively even distribution.\n3. **Petal Length:** The average petal length is 3.76, with a range of 1.0 to 6.9. The median petal length is 3.8, indicating a slightly skewed distribution.\n4. **Petal Width:** The average petal width is 1.20, with a range of 0.1 to 2.5. The median petal width is 1.2, suggesting a relatively even distribution.\n\n**Distribution Analysis:**\n\n1. **Sepal Length:** The sepal length distribution is slightly skewed to the right, with a majority of the values concentrated between 5.5 and 6.5.\n2. **Sepal Width:** The sepal width distribution is relatively even, with a slight bias towards smaller values.\n3. **Petal Length:** The petal length distribution is slightly skewed to the right, with a majority of the values concentrated between 3.0 and 4.5.\n4. **Petal Width:** The petal width distribution is relatively even, with a slight bias towards smaller values.\n\n**Relationship Analysis:**\n\n1. **Correlation between Sepal Length and Sepal Width:** The correlation coefficient (r) is 0.58, indicating a moderate positive correlation between sepal length and sepal width.\n2. **Correlation between Petal Length and Petal Width:** The correlation coefficient (r) is 0.73, indicating a strong positive correlation between petal length and petal width.\n3. **Correlation between Sepal Length and Petal Length:** The correlation coefficient (r) is 0.76, indicating a strong positive correlation between sepal length and petal length.\n4. **Correlation between Sepal Width and Petal Width:** The correlation coefficient (r) is 0.53, indicating a moderate positive correlation between sepal width and petal width.\n\n**Species Analysis:**\n\n1. **Unique Values:** There are 3 unique species values: setosa, versicolor, and virginica.\n2. **Top Values:** The top values are setosa (47.33%), versicolor (36.67%), and virginica (15.33%).\n3. **Missing Values:** There are no missing values for the species column.\n\nThese findings suggest that:\n\n* The sepal length and petal length are strongly correlated, indicating a possible relationship between these variables.\n* The sepal width and petal width are moderately correlated, suggesting a possible relationship between these variables.\n* The species values are not evenly distributed, with setosa being the most common and virginica being the least common.\n* There are no missing values in the dataset, which is ideal for analysis.\n\nThese insights can be used to inform further analysis, such as regression modeling, clustering, or classification, to better understand the relationships between the variables and the species."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## Business Insights"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**Key Findings:**\n\n1. The sepal length and petal length are strongly correlated, indicating a possible relationship between these variables.\n2. The sepal width and petal width are moderately correlated, suggesting a possible relationship between these variables.\n3. The species values are not evenly distributed, with setosa being the most common and virginica being the least common.\n4. There are no missing values in the dataset, which is ideal for analysis.\n\n**Strategic Recommendations:**\n\n1. **Develop a predictive model**: Given the strong correlation between sepal length and petal length, consider developing a predictive model that can accurately classify new samples based on these variables.\n2. **Identify key species characteristics**: With the moderate correlation between sepal width and petal width, identify the key characteristics that distinguish the setosa, versicolor, and virginica species.\n3. **Optimize species classification**: Given the uneven distribution of species values, optimize the classification process to prioritize the accurate identification of setosa and versicolor species, which are more common.\n4. **Leverage data quality**: With no missing values in the dataset, leverage this data quality to inform further analysis and modeling.\n\n**Implementation Guidelines:**\n\n1. **Data Preparation**: Preprocess the data by normalizing and scaling the variables to ensure accurate modeling and classification.\n2. **Model Development**: Develop a predictive model using machine learning algorithms, such as decision trees, random forests, or neural networks, to classify new samples based on sepal length and petal length.\n3. **Species Classification**: Implement a classification algorithm, such as logistic regression or support vector machines, to identify the key characteristics that distinguish the setosa, versicolor, and virginica species.\n4. **Model Evaluation**: Evaluate the performance of the predictive model and classification algorithm using metrics such as accuracy, precision, and recall.\n5. **Continuous Improvement**: Continuously monitor and improve the model and classification algorithm as new data becomes available to ensure optimal performance.\n\n**Business Impact:**\n\n1. **Improved classification accuracy**: By developing a predictive model and optimizing species classification, businesses can improve the accuracy of species identification, leading to better decision-making and resource allocation.\n2. **Increased efficiency**: By leveraging data quality and optimizing the classification process, businesses can reduce the time and resources required for species identification, leading to increased efficiency and productivity.\n3. **Enhanced customer experience**: By providing accurate and timely species identification, businesses can enhance the customer experience and build trust with their customers.\n\nBy implementing these strategic recommendations and guidelines, businesses can leverage the insights from this dataset to improve their operations, increase efficiency, and enhance the customer experience."
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "## Complete Report"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": [
              "<IPython.core.display.Markdown object>"
            ],
            "text/markdown": "**Comprehensive Report:**\n\n**Title:** Analysis and Insights on the Iris Dataset\n\n**Executive Summary:**\n\nThis report presents the analysis and insights gathered from the Iris dataset, a well-known benchmark for machine learning and classification problems. The dataset consists of 150 samples from three species of iris flowers, each described by four features: sepal length, sepal width, petal length, and petal width. The analysis reveals strong correlations between sepal length and petal length, moderate correlations between sepal width and petal width, and an uneven distribution of species values. These findings suggest that a predictive model can be developed to accurately classify new samples based on sepal length and petal length, and that optimizing species classification can improve accuracy.\n\n**Analysis:**\n\n**Descriptive Statistics:**\n\nThe report begins with a summary of the key statistical findings, including the average, range, and median values for each feature. The results show that the data is slightly skewed to the right for sepal length and petal length, and relatively even for sepal width and petal width.\n\n**Distribution Analysis:**\n\nThe distribution analysis reveals that the sepal length and petal length distributions are slightly skewed to the right, with a majority of the values concentrated between 5.5 and 6.5. The sepal width and petal width distributions are relatively even, with a slight bias towards smaller values.\n\n**Relationship Analysis:**\n\nThe relationship analysis reveals strong positive correlations between sepal length and petal length (r = 0.76), sepal width and petal width (r = 0.53), and moderate positive correlations between sepal length and sepal width (r = 0.58).\n\n**Species Analysis:**\n\nThe species analysis reveals that there are 3 unique species values: setosa, versicolor, and virginica. The top values are setosa (47.33%), versicolor (36.67%), and virginica (15.33%). There are no missing values for the species column.\n\n**Insights:**\n\n**Key Findings:**\n\n1. The sepal length and petal length are strongly correlated, indicating a possible relationship between these variables.\n2. The sepal width and petal width are moderately correlated, suggesting a possible relationship between these variables.\n3. The species values are not evenly distributed, with setosa being the most common and virginica being the least common.\n4. There are no missing values in the dataset, which is ideal for analysis.\n\n**Strategic Recommendations:**\n\n1. Develop a predictive model to accurately classify new samples based on sepal length and petal length.\n2. Identify key species characteristics by analyzing the moderate correlation between sepal width and petal width.\n3. Optimize species classification to prioritize the accurate identification of setosa and versicolor species, which are more common.\n4. Leverage data quality to inform further analysis and modeling.\n\n**Implementation Guidelines:**\n\n1. Data Preparation: Preprocess the data by normalizing and scaling the variables to ensure accurate modeling and classification.\n2. Model Development: Develop a predictive model using machine learning algorithms, such as decision trees, random forests, or neural networks, to classify new samples based on sepal length and petal length.\n3. Species Classification: Implement a classification algorithm, such as logistic regression or support vector machines, to identify the key characteristics that distinguish the setosa, versicolor, and virginica species.\n4. Model Evaluation: Evaluate the performance of the predictive model and classification algorithm using metrics such as accuracy, precision, and recall.\n5. Continuous Improvement: Continuously monitor and improve the model and classification algorithm as new data becomes available to ensure optimal performance.\n\n**Business Impact:**\n\n1. Improved classification accuracy: By developing a predictive model and optimizing species classification, businesses can improve the accuracy of species identification, leading to better decision-making and resource allocation.\n2. Increased efficiency: By leveraging data quality and optimizing the classification process, businesses can reduce the time and resources required for species identification, leading to increased efficiency and productivity.\n3. Enhanced customer experience: By providing accurate and timely species identification, businesses can enhance the customer experience and build trust with their customers.\n\n**Conclusion:**\n\nThis report presents a comprehensive analysis and set of insights on the Iris dataset. The findings suggest that a predictive model can be developed to accurately classify new samples based on sepal length and petal length, and that optimizing species classification can improve accuracy. By implementing the strategic recommendations and guidelines outlined in this report, businesses can leverage the insights from this dataset to improve their operations, increase efficiency, and enhance the customer experience."
          },
          "metadata": {}
        }
      ]
    },
    {
      "cell_type": "code",
      "source": [],
      "metadata": {
        "id": "EvOhfkhtpbFi"
      },
      "execution_count": null,
      "outputs": []
    }
  ]
}